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Linguistic Acceptability

Linguistic Acceptability is the task of determining whether a sentence is grammatical or ungrammatical.

Image Source: Warstadt et al

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 51–60 of 72 papers

Title	Date	Tasks	Status	Hype	Score
Bag of Tricks for Effective Language Model Pretraining and Downstream Adaptation: A Case Study on GLUE	Feb 18, 2023	Contrastive LearningDenoising	—Unverified	0	0
A Neural-Symbolic Approach Towards Identifying Grammatically Correct Sentences	Jul 16, 2023	ArticlesCoLA	—Unverified	0	0
An Automated Knowledge Mining and Document Classification System with Multi-model Transfer Learning	Jun 24, 2021	CoLADocument Classification	—Unverified	0	0
Linguistic Analysis of Pretrained Sentence Encoders with Acceptability Judgments	Jan 11, 2019	CoLAGeneral Classification	—Unverified	0	0
Revisiting the Uniform Information Density Hypothesis	Sep 23, 2021	Linguistic AcceptabilitySentence	—Unverified	0	0
Grammatical Analysis of Pretrained Sentence Encoders with Acceptability Judgments	Dec 11, 2018	CoLALinguistic Acceptability	—Unverified	0	0
Grammaticality and Language Modelling	Nov 1, 2020	CoLAExperimental Design	—Unverified	0	0
StructBERT: Incorporating Language Structures into Pre-training for Deep Language Understanding	Aug 13, 2019	Language ModelingLanguage Modelling	—Unverified	0	0
How well can machine-generated texts be identified and can language models be trained to avoid identification?	Oct 25, 2023	Linguistic AcceptabilityText Generation	—Unverified	0	0
Robust ASR Error Correction with Conservative Data Filtering	Jul 18, 2024	Automatic Speech RecognitionAutomatic Speech Recognition (ASR)	—Unverified	0	0

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All datasets CoLA RuCoLA CoLA Dev ItaCoLA DaLAJ

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	En-BERT + TDA + PCA	Accuracy	88.6	—	Unverified
2	BERT+TDA	Accuracy	88.2	—	Unverified
3	RoBERTa+TDA	Accuracy	87.3	—	Unverified
4	deberta-v3-base+tasksource	Accuracy	87.15	—	Unverified
5	RoBERTa-large 355M + Entailment as Few-shot Learner	Accuracy	86.4	—	Unverified
6	LTG-BERT-base 98M	Accuracy	82.7	—	Unverified
7	ELC-BERT-base 98M	Accuracy	82.6	—	Unverified
8	En-BERT + TDA	Accuracy	82.1	—	Unverified
9	FNet-Large	Accuracy	78	—	Unverified
10	LTG-BERT-small 24M	Accuracy	77.6	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Ru-RoBERTa+TDA	MCC	0.59	—	Unverified
2	ruRoBERTa	MCC	0.53	—	Unverified
3	Ru-BERT+TDA	MCC	0.48	—	Unverified
4	RemBERT	MCC	0.44	—	Unverified
5	ruBERT	MCC	0.42	—	Unverified
6	ruGPT-3	MCC	0.3	—	Unverified
7	ruT5	MCC	0.25	—	Unverified
8	mBERT	MCC	0.15	—	Unverified
9	XLM-R	MCC	0.13	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	En-BERT + TDA	Accuracy	88.6	—	Unverified
2	XLM-R (pre-trained) + TDA	Accuracy	73	—	Unverified
3	DeBERTa (large)	Accuracy	69.5	—	Unverified
4	TinyBERT-6 67M	Accuracy	54	—	Unverified
5	Synthesizer (R+V)	Accuracy	53.3	—	Unverified
6	En-BERT (pre-trained) + TDA	MCC	0.42	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	XLM-R + TDA	MCC	0.68	—	Unverified
2	XLM-R	MCC	0.52	—	Unverified
3	It-BERT (pre-trained) + TDA	MCC	0.48	—	Unverified
4	mBERT	MCC	0.36	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Sw-BERT + H0M	Accuracy	76.9	—	Unverified